


from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class 		__magic_name__						(snake_case_				):





     '''simple docstring'''



     __lowercase				:							Optional[int]	   =       ['image_processor', 'tokenizer']
     __lowercase				:							Optional[int]	   =       'BridgeTowerImageProcessor'
     __lowercase				:							int	   =       ('RobertaTokenizer', 'RobertaTokenizerFast')

     def __init__(					self:Union[str, Any]     ,		_a:Union[str, Any]     ,		_a:Any   ):
       super().__init__(_a     ,		_a   )

     def __call__(					self:str     ,		_a:Tuple     ,		_a:Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None     ,		_a:bool = True     ,		_a:Union[bool, str, PaddingStrategy] = False     ,		_a:Union[bool, str, TruncationStrategy] = None     ,		_a:Optional[int] = None     ,		_a:int = 0     ,		_a:Optional[int] = None     ,		_a:Optional[bool] = None     ,		_a:Optional[bool] = None     ,		_a:bool = False     ,		_a:bool = False     ,		_a:bool = False     ,		_a:bool = False     ,		_a:bool = True     ,		_a:Optional[Union[str, TensorType]] = None     ,		**_a:Dict     ,		):
       snake_case__						= self.tokenizer(
           text=_a     ,		add_special_tokens=_a     ,		padding=_a     ,		truncation=_a     ,		max_length=_a     ,		stride=_a     ,		pad_to_multiple_of=_a     ,		return_token_type_ids=_a     ,		return_attention_mask=_a     ,		return_overflowing_tokens=_a     ,		return_special_tokens_mask=_a     ,		return_offsets_mapping=_a     ,		return_length=_a     ,		verbose=_a     ,		return_tensors=_a     ,		**_a     ,		)
       # add pixel_values + pixel_mask
       snake_case__						= self.image_processor(
           _a     ,		return_tensors=_a     ,		do_normalize=_a     ,		do_center_crop=_a     ,		**_a   )
       encoding.update(_a   )

       return encoding

     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]     ,		*_a:Optional[int]     ,		**_a:Optional[Any]   ):
       return self.tokenizer.batch_decode(*_a     ,		**_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]     ,		*_a:Tuple     ,		**_a:List[str]   ):
       return self.tokenizer.decode(*_a     ,		**_a   )







     @property
     def 	SCREAMING_SNAKE_CASE__						(					self:int   ):
       snake_case__						= self.tokenizer.model_input_names
       snake_case__						= self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )





'''simple docstring'''


def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				print("""\nThe shortest path matrix using Floyd Warshall algorithm\n"""	)
				for i in range(SCREAMING_SNAKE_CASE__	):
								for j in range(SCREAMING_SNAKE_CASE__	):
												if dist[i][j] != float("""inf"""	):
																print(int(dist[i][j]	)				, end="""\t"""	)
												else:
																print("""INF"""				, end="""\t"""	)
								print()







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			Any							= [[float("""inf"""	) for _ in range(SCREAMING_SNAKE_CASE__	)] for _ in range(SCREAMING_SNAKE_CASE__	)]

				for i in range(SCREAMING_SNAKE_CASE__	):
								for j in range(SCREAMING_SNAKE_CASE__	):
												_SCREAMING_SNAKE_CASE					:			List[str]							= graph[i][j]

												# check vertex k against all other vertices (i, j)
				for k in range(SCREAMING_SNAKE_CASE__	):
								# looping through rows of graph array
								for i in range(SCREAMING_SNAKE_CASE__	):
												# looping through columns of graph array
												for j in range(SCREAMING_SNAKE_CASE__	):
																if (
																    dist[i][k] != float("""inf"""	)
																    and dist[k][j] != float("""inf"""	)
																    and dist[i][k] + dist[k][j] < dist[i][j]
																):
																				_SCREAMING_SNAKE_CASE					:			int							= dist[i][k] + dist[k][j]

				_print_dist(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)
				return dist, v


if __name__ == "__main__":
				UpperCAmelCase_						:  str				  =						int(input('Enter number of vertices: '))
				UpperCAmelCase_						:  Union[str, Any]				  =						int(input('Enter number of edges: '))

				UpperCAmelCase_						:  Dict				  =						[[float('inf') for i in range(v)] for j in range(v)]

				for i in range(v):
								UpperCAmelCase_						:  Union[str, Any]				  =						0.0

								# src and dst are indices that must be within the array size graph[e][v]
								# failure to follow this will result in an error
				for i in range(e):
								print('\nEdge ', i + 1)
								UpperCAmelCase_						:  int				  =						int(input('Enter source:'))
								UpperCAmelCase_						:  List[str]				  =						int(input('Enter destination:'))
								UpperCAmelCase_						:  List[str]				  =						float(input('Enter weight:'))
								UpperCAmelCase_						:  int				  =						weight

				floyd_warshall(graph, v)

				# Example Input
				# Enter number of vertices: 3
				# Enter number of edges: 2

				# # generated graph from vertex and edge inputs
				# [[inf, inf, inf], [inf, inf, inf], [inf, inf, inf]]
				# [[0.0, inf, inf], [inf, 0.0, inf], [inf, inf, 0.0]]

				# specify source, destination and weight for edge #1
				# Edge  1
				# Enter source:1
				# Enter destination:2
				# Enter weight:2

				# specify source, destination and weight for edge #2
				# Edge  2
				# Enter source:2
				# Enter destination:1
				# Enter weight:1

				# # Expected Output from the vertice, edge and src, dst, weight inputs!!
				# 0		INF	INF
				# INF	0	2
				# INF	1	0




